How does AI tokenization work?

Most AI models use Byte Pair Encoding (BPE) tokenization. Frequently occurring character sequences become single tokens; rare sequences split into smaller pieces. Common words are usually one token; punctuation and code symbols add extra tokens.

Why does code use more tokens than prose?

Code has more punctuation characters ({, }, (, ), ;, =, etc.) and uncommon identifier names that BPE splits into multiple subword tokens. A 100-word code snippet may use 150-200 tokens while 100 words of English prose uses ~130 tokens.

Is this the actual tokenizer?

No — this tool uses a word-based approximation that splits on whitespace, punctuation, and long words. The actual tokenizers (tiktoken for OpenAI, SentencePiece for LLaMA) produce different boundaries. Use this for intuition, not exact counts.

AI Tokenizer Visualizer

Visualize how AI models split text into tokens with color coding.

Model

Text input

Approx tokens15

Segments15

Characters59

Token visualization

Hello world! This is a test of the AI tokenizer visualizer.

Approximate visualization using word-based heuristics. Actual tokenization varies by model.

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FAQ

How does AI tokenization work?: Most AI models use Byte Pair Encoding (BPE) tokenization. Frequently occurring character sequences become single tokens; rare sequences split into smaller pieces. Common words are usually one token; punctuation and code symbols add extra tokens.
Why does code use more tokens than prose?: Code has more punctuation characters ({, }, (, ), ;, =, etc.) and uncommon identifier names that BPE splits into multiple subword tokens. A 100-word code snippet may use 150-200 tokens while 100 words of English prose uses ~130 tokens.
Is this the actual tokenizer?: No — this tool uses a word-based approximation that splits on whitespace, punctuation, and long words. The actual tokenizers (tiktoken for OpenAI, SentencePiece for LLaMA) produce different boundaries. Use this for intuition, not exact counts.

See how AI language models approximately break text into tokens. Each token segment is color-coded with alternating colors. Useful for understanding tokenization of code, punctuation, and uncommon words. Uses word-based approximation — not the actual BPE tokenizer.